THE EXPORT MARKET FOR DIFFERENTIATED PROCESSED AGRICULTURAL PRODUCTS: THE ROLE OF FACTOR PRICES AND FIXED COSTS

The theories of monopolistic competition and ¡°love for variety¡± contend that the differences in firms¡¯ prices and market shares arise from product differentiation, which is linked to firms¡¯ fixed costs. This paper reviews these theories and their implications for prices and market shares of firms from developing countries seeking to expand their exports of processed agricultural goods. The study proposes a model showing the role of the firms¡¯ costs as a source of product differentiation. Using econometric methods, the model estimates the firms¡¯ residual demand elasticities, which indicate the degree of product differentiation and market power. The model also determines the effects of the firms¡¯ own costs and competitors¡¯ costs on the residual demand and market shares. Case studies for cocoa products and roasted coffee in the U.S. import market are examined. Exporters to the U.S. include developing countries that produce the raw cocoa and coffee. The results show that high prices and large market shares are associated with high levels of product differentiation in these markets. Also, market shares increase with the level of fixed costs, which are measured by proxy as advertising expenditures. The implication for small firms in developing countries is that increasing the degree of product differentiation through increased investment in advertising or research and development could increase their market shares and their export revenues.International Relations/Trade,

Detection of denial of service attacks against domain name system using neural networks

In this paper we introduce an intrusion detection system for Denial of Service (DoS) attacks against Domain Name System (DNS). Our system architecture consists of two most important parts: a statistical preprocessor and a neural network classifier. The preprocessor extracts required statistical features in a shorttime frame from traffic received by the target name server. We compared three different neural networks for detecting and classifying different types of DoS attacks. The proposed system is evaluated in a simulated network and showed that the best performed neural network is a feed-forward backpropagation with an accuracy of 99%

Scalable Full Flow with Learned Binary Descriptors

We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a conditional random field (CRF). We tackle the computation- and memory-intensive operations on the 4D cost volume by a min-projection which reduces memory complexity from quadratic to linear and binary descriptors for efficient matching. This enables evaluation of the cost on the fly and allows to perform learning and CRF inference on high resolution images without ever storing the 4D cost volume. To address the problem of learning binary descriptors we propose a new hybrid learning scheme. In contrast to current state of the art approaches for learning binary CNNs we can compute the exact non-zero gradient within our model. We compare several methods for training binary descriptors and show results on public available benchmarks.Comment: GCPR 201

End-to-End Localization and Ranking for Relative Attributes

We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201

BATS: Binary ArchitecTure Search

This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS). We show that directly applying NAS to the binary domain provides very poor results. To alleviate this, we describe, to our knowledge, for the first time, the 3 key ingredients for successfully applying NAS to the binary domain. Specifically, we (1) introduce and design a novel binary-oriented search space, (2) propose a new mechanism for controlling and stabilising the resulting searched topologies, (3) propose and validate a series of new search strategies for binary networks that lead to faster convergence and lower search times. Experimental results demonstrate the effectiveness of the proposed approach and the necessity of searching in the binary space directly. Moreover, (4) we set a new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and ImageNet datasets. Code will be made available https://github.com/1adrianb/binary-nasComment: accepted to ECCV 202

Improvement in the Production of L-Lysine by Overexpression of Aspartokinase (ASK) in C. glutamicum ATCC 21799

Purpose: To clone Corynebacterium glutamicum ATCC21799 aspartokinase gene (EC 2.7.2.4) using shuttle expression vector pEKEx2 in order to increase lysine production.Methods: C. glutamicum DNA was extracted and used for amplification of aspartokinase gene (ask) by cloning into an E. coli/C. glutamicum shuttle expression vector, pEKEx2. Initially, the recombinant vector transformed into E. coli DH5á and then into C. glutamicum.Results: Electrophoresis of recombinant protein by SDS-PAGE showed that the molecular weight of the recombinant protein was 42 KD. The induction of recombinant vector by IPTG had an inhibitory effect on cell growth due to over-expression of the cloned gene. The results of lysine assay by Chinard method showed that lysine production increased about two-fold, compared with the parent strain, as a result of increased copy numbers of lysC gene in recombinant strain.Conclusion: A two-fold increase in lysine production was observed by cloning of the ASK gene in C. glutamicum rather than in E. coli, due to the presence of lysine exporter channel which facilitates lysine extraction.Keywords: LysC gene, Corynebacterium glutamicum, L- lysine, Cloning, Aspartokinase, E. col

Simultaneous Ad Auctions

We consider a model with two simultaneous VCG ad auctions A and B where each advertiser chooses to participate in a single ad auction. We prove the existence and uniqueness of a symmetric equilibrium in that model. Moreover, when the click rates in A are pointwise higher than those in B, we prove that the expected revenue in A is greater than the expected revenue in B in this equilibrium. In contrast, we show that this revenue ranking does not hold when advertisers can participate in both auctions

Prevalence and Predictors of Cesarean Section in Zanjan-Iran during 2014-2016

Background: The increased prevalence of cesarean section (C–section) is a global epidemic. Objectives: The aim of this study was to determine the prevalence and demographic, fertility, and childbirth-related factors of C–section in Zanjan province, Iran,-from 21 March 2014 to 19 March 2016. Methods: This study was a descriptive analytic study, carried out in 2014–2016, which gathered 41, 265 registered childbirth data in Zanjan province hospitals and from country electronic childbirth register system. Data were analyzed using descriptive, univariate and multivariate logistic binominal regression. Results: according to the findings, the prevalence of C–section was 40.1%. The odds of having C–section went up with increasing maternal age (OR=1.026), gravidity (OR=0.670), and gestational age (OR=0.093), while it decreased with an increased parity, end educational level up to high school graduate. In contrast, higher educational (OR=3.064) level increased the odds of having C–section. Living in the urban areas (OR=1.855) also increased the oddsof C–section. Diabetes (OR=1.990), preeclampsia or eclampsia (OR=2.350), hypertension (OR=1.983), and thyroid disorders (OR=2.289) increased the odds of having C–section. Newborns with low birth weight (OR=1) and macrosomia (OR=2.663), and boys (OR=1.107) were delivered more via C–section. Among the interventions during labor, induction (OR=1.131) and stimulation of labor (OR=0.269) reduced the odds of C–section (P<0.05). Conclusion: C–section rate is very high in Iran and its association with different variables can be a basis for planning and policymaking in order to reduce the C–section rate, particularly in Zanjan province

eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection. However, the size of LLMs (i.e., billions of parameters) requires highly effective compression to fit into storage-limited devices. Among many compression techniques, weight-clustering, a form of non-linear quantization, is one of the leading candidates for LLM compression, and supported by modern smartphones. Yet, its training overhead is prohibitively significant for LLM fine-tuning. Especially, Differentiable KMeans Clustering, or DKM, has shown the state-of-the-art trade-off between compression ratio and accuracy regression, but its large memory complexity makes it nearly impossible to apply to train-time LLM compression. In this paper, we propose a memory-efficient DKM implementation, eDKM powered by novel techniques to reduce the memory footprint of DKM by orders of magnitudes. For a given tensor to be saved on CPU for the backward pass of DKM, we compressed the tensor by applying uniquification and sharding after checking if there is no duplicated tensor previously copied to CPU. Our experimental results demonstrate that \prjname can fine-tune and compress a pretrained LLaMA 7B model from 12.6 GB to 2.5 GB (3bit/weight) with the Alpaca dataset by reducing the train-time memory footprint of a decoder layer by 130$\times$, while delivering good accuracy on broader LLM benchmarks (i.e., 77.7\% for PIQA, 66.1\% for Winograde, and so on).Comment: preprin

Quantized Densely Connected U-Nets for Efficient Landmark Localization

In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused across the stacked U-Nets. This dense connectivity largely improves the information flow, yielding improved localization accuracy. However, a vanilla dense design would suffer from critical efficiency issue in both training and testing. To solve this problem, we first propose order-K dense connectivity to trim off long-distance shortcuts; then, we use a memory-efficient implementation to significantly boost the training efficiency and investigate an iterative refinement that may slice the model size in half. Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers. We validate our approach in two tasks: human pose estimation and face alignment. The results show that our approach achieves state-of-the-art localization accuracy, but using ~70% fewer parameters, ~98% less model size and saving ~75% training memory compared with other benchmark localizers. The code is available at https://github.com/zhiqiangdon/CU-Net.Comment: ECCV201